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"Gait variability has been studied in various diseases and in aging, however variability is observed even in young, healthy adults. Variability in stride time can be characterized in terms of short-term, or step-by-step variability, and long-term variability. It is plausible that these temporal parameters in gait have similar neural origins to the dual modes of cognitive control since both require goal-oriented, higher-order processing. A handful of frontoparietal areas have been widely observed to be important for the dynamic nature of these abilities. The purpose of this study is to test the hypothesis that the adaptability of this frontoparietal network, as defined by the variability of the blood oxygen level-dependent (BOLD) signal, underlies a relationship between cognitive control strategies and stride time variability. We recruited twenty healthy young adults between 18 and 35 years old (10 females; average age = 23.6 ± 3.9 years old) to measure performance on a stepping-in-place task, cognitive control, and BOLD signal variability using resting-state functional MRI. A Pearson correlation wase used to determine the association between proactive and reactive cognitive control strategies and long and short-term gait variability, and a partial least squares correlation was used to determine if there is a pattern of BOLD signal variability in a set of frontoparietal regions that jointly explains these cognitive-gait relationships. There was no relationship between cognitive control strategy and long- or short-term variability, however there was a pattern of BOLD variability, primarily in control and salience/ventral attention network regions, that was associated with both short-term gait variability, and to a lesser extent, long-term gait variability (Permutation p = 0.0323). These findings provide evidence of gait variability as a marker of brain variability in healthy, young adults and may open the door to understanding its role as a biomarker of brain health."--Abstract from author supplied metadata.

"Over the last 40 years, a new paradigm has been posited where the variability observed in physiological systems is a consequence of the interactions occurring between the various components that affect the system. While quantifying the magnitude of variability can be useful, analyses that measure how the structure of the variability (dynamics) changes over time have been posited to reflect the health of the system. Many researchers interpret the results of these analyses to be indicative of the system's adaptive capacity. While there is ample indirect evidence to support this notion, a lack of direct findings has left the literature lacking a definitive foundation to move forward with this interpretation. While many physiological systems are too invasive to safely perturb, the movement-based systems are routinely perturbed in real-world environments without dire consequences. Of particular interest is the locomotor system, which is constantly challenged in real-world environments via slips and trips. Furthermore, the locomotor system can be safely and validly perturbed in the laboratory. A range of locomotor dynamics-based measures have been used to describe differences between various clinical populations, but none have been directly associated with a person's ability to remain upright when perturbed. The objectives of this study are to (1) examine the relationship between locomotor dynamics/stability to overall fall-risk prior, (2) examine how locomotor dynamics relate to the ability to recover from a trip via global stability, and (3) determine the extent to which an acute trip-training session alters locomotor dynamics and global stability. Forty healthy, older adults (75.2±4.9 yrs) were recruited by convenience from the local community. The participants completed a variety of clinical assessments in order to determine overall fall-risk. Afterwards, they participated in three walking trials consisting of: 1) a 15-minute unperturbed walking session, 2) a 10-minute unperturbed walking session (control) or a 10-minute trip-training session (intervention), and 3) a 15-minute unperturbed walking session. Various measurements of locomotor dynamics and adaptability were calculated from full-body 3-D kinematics collected at 100Hz. Multiple regression and repeated measure analysis of variance models were calculated to determine to what extent locomotor dynamics and adaptability relate to one another and how an acute trip-training session affects their relationship. The results from our first experiment suggested that locomotor dynamics and stability during steady state do not significantly relate to overall fall-risk. However, the second experiment showed that locomotor dynamics are predictive of an individual's ability to recover from a trip. Our last experiment showed the feasibility of using an acute trip-training session to alter locomotor dynamics and stability. These data represent the first direct evidence of physiological variability being indicative of adaptive capacity in the locomotor system. Further investigation will be necessary to determine the robustness of the analyses to indicate adaptive capacity across perturbations and populations."--Abstract from author supplied metadata.

"Studying the variability of physiological subsystems (e.g., cardiac and locomotor control systems) has been insightful in understanding how functional and dysfunctional patterns emerge within their behaviors. The coupling of these subsystems (termed cardiolocomotor coupling) is believed to be important to maintain healthy functioning in the diverse conditions in which individuals must operate. Aging and pathology result in alterations to both the patterns of individual systems, as well as to how those systems couple to each other. By examining cardiac and locomotor rhythms concurrently during treadmill walking, it is possible to ascertain how these two rhythms relate to each other in different populations (i.e., younger and older adults) and with varying task constraints (i.e., a gait synchronization task or fast walking task). The purpose of this research was to simultaneously document the characteristics of cardiac and gait rhythms in younger (18-35 yrs) and older (63-80 yrs) healthy adults while walking and to establish the extent to which changes in these systems are coupled when gait is constrained. This study consisted of two repeated-measures experiments that participants completed on two separate days. Both experiments consisted of three 15-minute phases. During the first (baseline) and third (retention) phases of both experiments, participants walked with no cues or specific instructions at their preferred walking speed. During the second phase, participants were asked to synchronize their step falls to the timing of a visual cue (experiment 1) or to walk at 125% of their preferred walking speed (experiment 2). Fifty-one healthy adults (26 older, 67.67±4.70 yrs, 1.72±0.09 m, 70.13±14.30 kg; 25 younger, 24.57±4.29 yrs, 1.76±0.09 m, 73.34±15.35 kg) participated in this study. Participants' cardiac rhythms (R-R interval time series) and locomotor rhythms (stride interval, step width, and step length time series) were measured while walking on a treadmill. Characteristics of variability in cardiac and locomotor rhythms and the coupling between cardiac and gait rhythms were measured. Results revealed that younger and older healthy adults alter gait patterns similarly when presented with a gait synchronization or fast walking task and that these tasks also alter cardiac patterns. Likewise, both groups exhibited enhanced cardiolocomotor coupling when tasked with a step timing constraint or increased speed during treadmill walking. Combined, these findings suggest that walking tasks likely alter both locomotor and cardiac dynamics and the coupling of physiological subsystems could be insightful in understanding the diverse effects aging and pathology have on individuals."--Abstract from author supplied metadata.

"Recent literature suggests that gait dynamics plays a role in establishing healthy, adaptive gait behavior, and that illness or injury can alter the dynamic patterns of gait (termed fractal patterns). So called "dynamical diseases" change the fractal patterns in gait, hereby reducing adaptive gait ability and increasing fall-risk. Previous research has shown that fractal patterns in gait can be strengthened through the use of a fractal metronome stimulus. However, in previous research participants have consistently presented weaker fractal patterns than prescribed by the metronome, despite improvements from their baseline. One postulate is that this gap between the stimulus and the participants' response is due to the prescriptive nature of the stimulus - that is, the metronome is presented with no interaction with the user. If so, the introduction of real-time feedback regarding synchrony with the stimulus may be beneficial to strengthening fractal patterns. The purpose of this study was to examine the role of feedback in increasing synchrony with a fractal metronome stimulus, and in entraining fractal gait patterns. There were three hypotheses: First, feedback would elicit a stronger coupling between participants' gait dynamics and the dynamics of the stimulus relative to a non-feedback condition. Second, the addition of feedback to the visual metronome would lead to a stronger fractal pattern during the training and post-training (retention) phases. Third, participants with the strongest coupling during training would exhibit the strongest fractal patterns during training and post training. Results showed no difference in coupling between feedback and non-feedback conditions. The addition of feedback to the fractal metronome lead to no significant difference in fractal strength from baseline to training and baseline to retention. While greater coupling was correlated to stronger fractal patterns during training, there was no relationship between coupling and retention. This study provided further evidence supporting the use of metronomes to alter gait dynamics, and was one of the first to examine feedback in conjunction with fractal gait training."--Abstract from author supplied metadata.

"Our view of certain gait disorders has changed with the development of new interventions for gait training. Recent gait research has suggested that humans do not walk with metronomic patterns, but with fractal patterns. Fractal patterns which are a common characteristic across nearly every human sub-system when healthy (e.g., gait, balance, heart rate, breathing rate, ect.). The patterns suggest that variability within a system is adaptive and healthy as opposed to the once held position that variability within a system was maladaptive. However, with the discovery of gait variability comes new questions about how to best help those who suffer from abnormal gait. One new area of research proposes that metronomes providing a variable stimulus, called fractal metronomes, could be used to redevelop adaptive patterns in clinical populations with abnormal gait. While immediate retention of these newly developed patterns has been shown immediately after and 5 minutes after a single session of training, the patterns revert back to pre-training levels after 24 hours, suggesting that there is a dosage effect associated with this type of training. Longer-term retention is needed to ensure that these interventions have promise in helping patients with a pathology. Therefore, the goal of this thesis was to explore how seven days of training to a fractal metronome would impact the retention of new fractal gait characteristics. It was hypothesized that: (1) participants would exhibit a stronger coupling of their gait to the fractal stimulus with increased practice (i.e., assessed via cross correlations across days) and (2) immediate retention (i.e., directly after training) and longer-term retention (i.e., 24 hours after training) would increase as a function of increased practice. Hypothesis 1 was supported with the observation that the cross-correlation between and with subjects increased across the seven days of training. However, that did not translate to immediate or longer-term retention, thus hypothesis 2 was not supported. It is important to note that embedded within the testing for hypothesis 2 was the observation that participants' fractal patterns got stronger during the training phase across the seven days, it was just not transferred to the overground walking that was used to examine immediate and longer-term retention. The results of this study suggest that fractal gait patterns can be strengthened with multiple days of training. However, future work should provide fractal gait training overground and then test retention overground to remove the confound of training on a treadmill and then retention testing overground."--Abstract from author supplied metadata.

EMG classification is widely used in electric control of mechanically developed prosthesis, robots development, clinical application etc. It has been evaluated for years, but the main goal of this research is to develop an easy to implement and fast to execute pattern recognition method for classifying signals used for human gait analysis. This method is based on adding two new temporal features (form factor and standard deviation) for EMG signal recognition and using them along with several popular features (area under the curve, wavelength function-pathway and zero crossing rate) to come up with a low complexity suitable feature extraction. Results are presented for EMG data and a comparison with existing methods is made to validate the applicability of the foregoing method. It is shown that the best combination in terms of accuracy and time performance is given by spectral and temporal extraction features along with neural network recognition (NN) algorithm.

Biometrics now affect many people's lives, and is the focus of much academic research and commercial development. Gait is one of the most recent biometrics, with its own unique advantages. Gait recognizes people by the way they walk and run, analyzes movement, which in turn implies analyzing sequences of images. This professional book introduces developments from the laboratories of very distinguished researchers within this relatively new area of biometrics and clearly establishes human gait as a biometric. Human Identification Based on Gait provides a snapshot of all the biometric work in human identification by gait (all major centers for research are indicated in this book). To complete the picture, studies are included from medicine, psychology and other areas wherein we find not only justification for the use of gait as a biometric, but also pointers to techniques and to analysis. Human Identification Based on Gait is designed for a professional audience, composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science.